Probability of Infectious Disease in Humans during Epidemic

Popular SIR models and their modifications used to generate predictions about epidemics and, specifically, the COVID-19 pandemic, are inadequate. The aim of this study was to find the laws describing the probability of infection in a biological object. Using theoretical methods of research based on the probability theory, we constructed the laws describing the probability of infection in a human depending on the infective dose and considering the temporal characteristics of a given infection. The so-called generalized time-factor law, which factors in the time of onset and the duration of an infectious disease, was found to be the most general. Among its special cases are the law describing the probability of infection developing by some point in time t, depending on the infective dose, and the law that does not factor in the time of onset. The study produced a full list of quantitative characteristics of pathogen virulence. The laws described in the study help to solve practical tasks and should lie at the core of mathematical epidemiological modeling.Copyright © 2022 Obstetrics, Gynecology and Reproduction. All rights reserved.

CORONAVIRUS DISEASE SURVIVAL TIME MODEL FOR FORECASTING

The term "survival analysis" refers to statistical techniques for data analysis where the time until the occurrence of the desired event serves as the outcome variable. Time to event analysis is another name for survival analysis. Applications for survival analysis are fairly broad and include things like calculating a population's survival rate or contrasting the survival of two or more groups. Cox regression analysis is a highly well-liked and frequently applied technique among them. Data on disease states are typically obtained at random epochs or at periodic epochs during follow-up in research looking at biological changes between states of Coronavirus infection and the start of COVID-19 in the human immune system. For instance, after the COVID enters a person's bloodstream by a route of transmission, it progresses through numerous stages that are linked to the depletion of B cells before becoming COVID-19. This study presents the Cox's approach for simulating the link between variables influencing the development of two disease states, namely I= the time epoch of COVID infection and P= the time epoch of COVID-19. Incubation period (IP) or survival time is the precise interval of time between "P and I." It is shown how Cox's model works with several personal infective factors and how well it can estimate the percentage of COVID-19 victims with the same completed length of IP. Such forecast values are then established for a synthetically simulated data set. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19.

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Estimate the incubation period of coronavirus 2019 (COVID-19).

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

Analysis of a Typical COVID-19 Outbreak Spread by Tourist Groups

Background: Novel coronavirus disease (SARS-COV-2 infection or COVID-19) is a respiratory tract infection that has been linked to severe acute respiratory syndrome transmitted particularly through touching and respiration. The purpose of this study is to understand the epidemiological characteristics of COVID-19 cases in a typical tourist-related outbreak and explore the possible route for its transmission. Method(s): All data and epidemiological survey reports of COVID-19 cases in the outbreak were reported by provincial and urban (county) Centers for Disease Control and Prevention and Health Commissions nationwide from October 16th to November 5th, 2021. The epidemiological survey reports included information on gender, age, source of infection (imported from other provinces or locally acquired), daily life track and itinerary, date of symptom onset, and date of diagnosis. The data were analyzed using descriptive statistical methods, one-way analysis of variance, independent t-test, and Chi-square tests. Histograms and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. Result(s): The COVID-19 outbreak associated with the tourist groups has involved 551 COVID-19 cases, with a median age of 44 years (interquartile range: 30-59 years), gradually spreading from the northwestern region to the national level across 15 provinces of China. One-fifth of the cases (16.0%) had traveled to Ejin Banner, resulting in 68 second-generation cases. We estimated an outbreak on 11 flights and 19 trains, accounting for a total of 27 confirmed cases. In addition, 42 clusters of outbreak cases were also reported to occur, 21 (50.0%) in households and 10 (23.81%) in restaurants. About 106 confirmed cases were related to the gatherings in restaurants. The median incubation period for this COVID-19 outbreak was 7 days (inter-quartile range: 5-10 days). Conclusion(s): The survey results indicated that this COVID-19 outbreak originated in Ejin Banner and was spread by tourist groups, which was a typical infection outbreak promoted by travel. Our results further confirmed that travel needs to be more strictly weighed in pandemics like COVID-19, and people need to pay more attention to the prevention against infectious diseases, particularly when traveling in a tourist group.Copyright © 2022 Zheng et al.

Clinical Characteristics of a Group of Patients Confirmed with COVID-19 at a Hospital in Tehran, Iran: A Retrospective Case Report

Introduction: The present study examines clinical features of patients infected with the 2019 sever acute respiratory syndrome coronavirus 2(SARS-Cov-2) leading to the coronavirus disease 2019 (covid-2019) in Rasool Akram hospital, Tehran, Iran. Material(s) and Method(s): This was a retrospective case report performed at Rasool Akram hospital, Tehran, Iran. A total of 77 patients referred to the hospital with SARS-Covid-2 infection. Data of the present study has been collected from March 5th to April 5th 2020. Result(s): Intensive care unit (ICU) has admitted 20 patients out of 77 patients. Among this sample, 23 patients were infected with acute respiratory syndrome and the other 18 remaining passed away. The calculated mean age of the patients admitted to the ICU was 60.8, 18 out of whom had deceased. In our results, male patients outnumber female patients where male patients account for 62.33% and female patients account for 37.66% of the whole study population. The most frequent and usual sings of this disease first reported as respirational distress or dyspnea (54.54%), coughs (54.54%) and myalgia (25.97). Only 3.89% of the patients had chest pain or chest discomfort. The most common comorbidities among those patients taken in the ICU and or deceased were diabetes, cardiovascular problems, hypertension and endocrine system problems. Out of 18 deaths, 11 (61.11%) cased had comorbidities. Among radiography and CT-scan results, 62.79% of the patients had involvement on chest radiography and 98.15% of the patients showed consolidation with ground glass opacities and 83.33% showed pleural effusion on their scan results. Conclusion(s): Having as much thorough information as possible about the characteristics of the patients infected with this virus helps us make better and sooner judgmental calls and more accurate diagnosis.Copyright © 2020 Ubiquity Press. All rights reserved.

Serial Intervals and Incubation Periods of SARS-CoV-2 Omicron and Delta Variants, Singapore.

We compared serial intervals and incubation periods for SARS-CoV-2 Omicron BA.1 and BA.2 subvariants and Delta variants in Singapore. Median incubation period was 3 days for BA.1 versus 4 days for Delta. Serial interval was 2 days for BA.1 and 3 days for BA.2 but 4 days for Delta.

Comparing the incubation period, serial interval, and infectiousness profile between SARS-CoV-2 Omicron and Delta variants.

In January 2022, the SARS-CoV-2 Omicron variants initiated major outbreaks and dominated the transmissions in Hong Kong, displacing an earlier outbreak seeded by the Delta variants. To provide insight into the transmission potential of the emerging variants, we aimed to compare the epidemiological characteristics of the Omicron and Delta variants. We analyzed the line-list clinical and contact tracing data of the SARS-CoV-2 confirmed cases in Hong Kong. Transmission pairs were constructed based on the individual contact history. We fitted bias-controlled models to the data to estimate the serial interval, incubation period and infectiousness profile of the two variants. Viral load data were extracted and fitted to the random effect models to investigate the potential risk modifiers for the clinical viral shedding course. Totally 14 401 confirmed cases were reported between January 1 and February 15, 2022. The estimated mean serial interval (4.4 days vs. 5.8 days) and incubation period (3.4 days vs. 3.8 days) were shorter for the Omicron than the Delta variants. A larger proportion of presymptomatic transmission was observed for the Omicron (62%) compared to the Delta variants (48%). The Omicron cases had higher mean viral load over an infection course than the Delta cases, with the elder cases appearing more infectious than the younger cases for both variants. The epidemiological features of Omicron variants were likely an obstacle to contact tracing measures, imposed as a major intervention in settings like Hong Kong. Continuously monitoring the epidemiological feature for any emerging SARS-CoV-2 variants in the future is needed to assist officials in planning measures for COVID-19 control.

SARS-CoV-2 Incubation Period during the Omicron BA.5-Dominant Period in Japan.

The mean virus incubation period during the SARS-CoV-2 Omicron BA.5-dominant period in Japan was 2.6 (95% CI 2.5-2.8) days, which was less than during the Delta-dominant period. Incubation period correlated with shared meals and adult infectors. A shorter incubation suggests a shorter quarantine period for BA.5 than for other variants.

Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data.

Quantitative information on epidemiological quantities such as the incubation period and generation time of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is scarce. We analysed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period for the Alpha and Delta variants using information on negative polymerase chain reaction tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time using a Bayesian inference approach applied to 9724 SARS-CoV-2 cases clustered in 3545 households where at least one secondary case was recorded. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4-5.4) for Alpha and 4.5 days (95% CrI 4.0-5.0) for Delta. The intrinsic generation time was estimated to have a mean of 7.12 days (95% CrI 6.27-8.44) for Alpha and of 6.52 days (95% CrI 5.54-8.43) for Delta. The household serial interval was 2.43 days (95% CrI 2.29-2.58) for Alpha and 2.74 days (95% CrI 2.62-2.88) for Delta, and the estimated proportion of pre-symptomatic transmission was 48-51% for both variants. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.

Epidemiology of SARS-CoV-2 Omicron BA.5 Infections, Macau, June-July 2022.

A SARS-CoV-2 Omicron BA.5 outbreak occurred in Macau from mid-June through July 2022. Out of >1,800 laboratory-confirmed cases, most were mild or asymptomatic; only 6 deaths were recorded. The outbreak was controlled through stringent public health and social measures, such as repeated universal testing and a stay-at-home order lasting 2 weeks.

Analysis of a Typical COVID-19 Outbreak Spread by Tourist Groups

Background: Novel coronavirus disease (SARS-COV-2 infection or COVID-19) is a respiratory tract infection that has been linked to severe acute respiratory syndrome transmitted particularly through touching and respiration. The purpose of this study is to understand the epidemiological characteristics of COVID-19 cases in a typical tourist-related outbreak and explore the possible route for its transmission. Method(s): All data and epidemiological survey reports of COVID-19 cases in the outbreak were reported by provincial and urban (county) Centers for Disease Control and Prevention and Health Commissions nationwide from October 16th to November 5th, 2021. The epidemiological survey reports included information on gender, age, source of infection (imported from other provinces or locally acquired), daily life track and itinerary, date of symptom onset, and date of diagnosis. The data were analyzed using descriptive statistical methods, one-way analysis of variance, independent t-test, and Chi-square tests. Histograms and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. Result(s): The COVID-19 outbreak associated with the tourist groups has involved 551 COVID-19 cases, with a median age of 44 years (interquartile range: 30-59 years), gradually spreading from the northwestern region to the national level across 15 provinces of China. One-fifth of the cases (16.0%) had traveled to Ejin Banner, resulting in 68 second-generation cases. We estimated an outbreak on 11 flights and 19 trains, accounting for a total of 27 confirmed cases. In addition, 42 clusters of outbreak cases were also reported to occur, 21 (50.0%) in households and 10 (23.81%) in restaurants. About 106 confirmed cases were related to the gatherings in restaurants. The median incubation period for this COVID-19 outbreak was 7 days (inter-quartile range: 5-10 days). Conclusion(s): The survey results indicated that this COVID-19 outbreak originated in Ejin Banner and was spread by tourist groups, which was a typical infection outbreak promoted by travel. Our results further confirmed that travel needs to be more strictly weighed in pandemics like COVID-19, and people need to pay more attention to the prevention against infectious diseases, particularly when traveling in a tourist group. Copyright © 2022 Zheng et al.

Correlation between times to SARS-CoV-2 symptom onset and secondary transmission undermines epidemic control efforts.

Severe acute respiratory coronavirus 2 (SARS-CoV-2) infections have been associated with substantial presymptomatic transmission, which occurs when the generation interval-the time between infection of an individual with a pathogen and transmission of the pathogen to another individual-is shorter than the incubation period-the time between infection and symptom onset. We collected a dataset of 257 SARS-CoV-2 transmission pairs in Japan during 2020 and jointly estimated the mean incubation period of infectors (4.8 days, 95 % CrI: 4.4-5.1 days), mean generation interval to when they infect others (4.3 days, 95 % credible interval [CrI]: 4.0-4.7 days), and the correlation (Kendall's tau: 0.5, 95 % CrI: 0.4-0.6) between these two epidemiological parameters. Our finding of a positive correlation and mean generation interval shorter than the mean infector incubation period indicates ample infectiousness before symptom onset and suggests that reliance on isolation of symptomatic COVID-19 cases as a focal point of control efforts is insufficient to address the challenges posed by SARS-CoV-2 transmission dynamics.

SARS-Cov-2 incubation period according to vaccination status during the fifth COVID-19 wave in a tertiary-care center in Spain: a cohort study.

BACKGROUND: The incubation period of an infectious disease is defined as the elapsed time between the exposure to the pathogen and the onset of symptoms. Although both the mRNA-based and the adenoviral vector-based vaccines have shown to be effective, there have been raising concerns regarding possible decreases in vaccine effectiveness for new variants and variations in the incubation period. METHODS: We conducted a unicentric observational study at the Hospital Universitari de Bellvitge, Barcelona, using a structured telephone survey performed by trained interviewers to estimate the incubation period of the SARS-CoV-2 Delta variant in a cohort of Spanish hospitalized patients. The distribution of the incubation period was estimated using the generalized odds-rate class of regression models. RESULTS: From 406 surveyed patients, 242 provided adequate information to be included in the analysis. The median incubation period was 2.8 days (95%CI: 2.5-3.1) and no differences between vaccinated and unvaccinated patients were found. Sex and age are neither shown not to be significantly related to the COVID-19 incubation time. CONCLUSIONS: Knowing the incubation period is crucial for controlling the spread of an infectious disease: decisions on the duration of the quarantine or on the periods of active monitoring of people who have been at high risk of exposure depend on the length of the incubation period. Furthermore, its probability distribution is a key element for predicting the prevalence and the incidence of the disease.

Pitfalls in Understanding and Handling of Covid-19 Vaccination ("Any Fool Can Know The Point Is to Understand"1)

The aim of this article is to critically review the managing of vaccination over the course of the present COVID-19 pandemic against the knowledge that had already been at hand and the scientific data that had yet to be learned. In the period before vaccines for COVID-19 became available, the startling similarity in epidemiologic behavior between COVID-19 and the Spanish flu could be observed. The development of vaccines against COVID-19 has evolved at an unprecedented speed resulting in highly im-munogenic vaccines with incredible protective characteristics covering a relatively short follow-up time in clinical trials. The roll-out in the general population turned out to take significantly longer time than the duration of immunity conferred by a 2-dose vaccination schedule (about 3-4 months). Therefore, the SARS-CoV-2 was left with the opportunity for random mutations with each replication cycle, resulting in immune evasion, shortened incubation, shortened serial interval, and increased transmissibility. The short incubation period of COVID-19 requires a steady protective antibody titer to be maintained to avert infection, achieve herd immunity, and terminate the pandemic spread. The protective neutralization titer needed to avert symptomatic infection and infection altogether is about 3% and 20%, respectively, of the mean convalescent titer. The latter corresponds to an absolute titer of 1:10-1:30. The intensity and duration of protective vaccinal and hybrid humoral immunity are explored. From the present perspective, it was naive to believe that a 2-dose vaccination would suffice to counter COVID-19 primarily due to its short incubation and a roll-out that was not catching up with the waning protective vaccinal antibody levels. Besides, the spacing of doses and boosters with respect to previous infection or vaccination, and differences in natural immunity and vaccine-induced immunity (adenovirus-vectored and mRNA) are discussed. The issue of vaccination and multisystem inflammatory syndrome in children is briefly presented. Finally, ethical points are discussed as some vaccine production platforms and neutralization tests use human cell lines derived from aborted fetuses. Conclusion - If the COVID-19 vaccines had been licensed as 3-dose vaccines, with more generous spacing, e.g. 0-2-6 months, providing for quantitatively larger and temporally more durable humoral immunity, that would have enabled attaining a steadier herd immunity and probably a paradoxical earlier effect on stopping the transmission. Copyright © 2022 by the University Clinical Centre Tuzla, Tuzla, Bosnia and Herzegovina.

The Association Between Clinical Severity and Incubation Period of SARS-CoV-2 Delta Variants: Retrospective Observational Study.

BACKGROUND: As of August 25, 2021, Jiangsu province experienced the largest COVID-19 outbreak in eastern China that was seeded by SARS-CoV-2 Delta variants. As one of the key epidemiological parameters characterizing the transmission dynamics of COVID-19, the incubation period plays an essential role in informing public health measures for epidemic control. The incubation period of COVID-19 could vary by different age, sex, disease severity, and study settings. However, the impacts of these factors on the incubation period of Delta variants remains uninvestigated. OBJECTIVE: The objective of this study is to characterize the incubation period of the Delta variant using detailed contact tracing data. The effects of age, sex, and disease severity on the incubation period were investigated by multivariate regression analysis and subgroup analysis. METHODS: We extracted contact tracing data of 353 laboratory-confirmed cases of SARS-CoV-2 Delta variants' infection in Jiangsu province, China, from July to August 2021. The distribution of incubation period of Delta variants was estimated by using likelihood-based approach with adjustment for interval-censored observations. The effects of age, sex, and disease severity on the incubation period were expiated by using multivariate logistic regression model with interval censoring. RESULTS: The mean incubation period of the Delta variant was estimated at 6.64 days (95% credible interval: 6.27-7.00). We found that female cases and cases with severe symptoms had relatively longer mean incubation periods than male cases and those with nonsevere symptoms, respectively. One-day increase in the incubation period of Delta variants was associated with a weak decrease in the probability of having severe illness with an adjusted odds ratio of 0.88 (95% credible interval: 0.71-1.07). CONCLUSIONS: In this study, the incubation period was found to vary across different levels of sex, age, and disease severity of COVID-19. These findings provide additional information on the incubation period of Delta variants and highlight the importance of continuing surveillance and monitoring of the epidemiological characteristics of emerging SARS-CoV-2 variants as they evolve.

A delayed modulation of solar ultraviolet radiation on the COVID-19 transmission reflects an incubation period

Laboratory experiments have revealed the meteorological sensitivity of the coronavirus disease 2019 (COVID-19) virus. However, no consensus has been reached about how outdoor meteorological conditions modulate the virus transmission as it is also constrained by non-meteorological conditions. Here, we identify the outbreak's evolution stage, constrained least by non-meteorological conditions, by searching the maximum correlation coefficient between the ultraviolet flux and the growth rate of cumulative confirmed cases at the country level. At this least-constrained stage, the cumulative cases count around 1300-3200, and the count's daily growth rate correlates with the ultraviolet flux and temperature significantly (correlation coefficients r = -0.54 +/- 0.09 and -0.39 +/- 0.10 at p<0.01$$ p, respectively), but not with precipitation, humidity, and wind. The ultraviolet correlation exhibits a delay of about 7 days, providing a meteorological measure of the incubation period. Our work reveals a seasonality of COVID-19 and a high risk of a pandemic resurgence in winter, implying a need for seasonal adaption in public policies.

Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States

COVID-19 has been declared as a “pandemic” by the World Health Organization (WHO) and has claimed more than a million lives and over 50 million confirmed cases worldwide as of 7th November 2020. This virus can be curbed in only two ways: vaccination and other by imposing non-pharmaceutical interventions (NPIs), which are behavioral changes to a person and community. Most of the nations worldwide have imposed NPIs in the form of social distancing and lockdowns, which have been effective in reducing the pace of the virus's spread, but continued implementation has deemed social and economic losses. Hence strategic implementation of NPIs in a burst of periods should be done based on educated decisions using data about population mobility trends to find hot zones that lead to a spike in cases. These decisions will positively impact the virus's spread with lower damage to social and economic aspects. © 2022 Elsevier Inc. All rights reserved.

The Road to SARS-CoV-2 Seasonality: A Vision of the Post-Pandemic Era

The end of the pandemic could be marked, not by the total eradication of the virus but by a decrease in cases and seasonal peaks in the frequency of SARSCoV-2. Although this has already happened with the influenza A (H1N1) pdm09 virus responsible for the 2009 pandemic, unlike on that occasion, many of the countries that have widely covered their population with the vaccination scheme, still receive the onslaught of COVID-19 and have resumed containment measures due to the appearance, above all, of new variants. The latter suggests that the path to SARS-CoV-2 seasonality may not be as benevolent as the 2009 influenza virus was. Therefore, it is necessary to study the characteristics by which this new virus can acquire seasonality. to consider this scenario and take the necessary measures to face it from a different perspective. © 2022 Academia Nacional de Medicina. All rights reserved.

Research progress on characteristics, epidemiology and control measure of SARS-CoV-2 Delta VOC

COVID-2019 has become a global pandemic, and a variety of SARS-CoV-2 variants have emerged with the continuous evolution and variation. SARS-CoV-2 Delta VOC (B.1.617.2) has the characteristics of strong transmission, short incubation period of infection, high pathogenicity and rapid disease progression, which has gradually become the main epidemic strain in India and even in the world, leading to countries and regions of the epidemic rebound. In this paper, the current epidemic characteristics and core control measures of SARS-CoV-2 Delta VOC was reviewed.